Libraries

library(tidyverse)
library(naniar)
library(visdat)
library(lubridate)
library(ggridges)
library(htmltools)
library(readr)
library(forcats)

# install.packages("devtools")
#devtools::install_github("dill/emoGG")
library(emoGG)
library(leaflet) # leaflet map

Data

Reading in and exploring the data.

longline <- readxl::read_xlsx("data/longline97-2008.xlsx")

palangre87_97 <- read_csv("data/palangre_87_97 - palangre_87_97.csv")

palangre97_18 <- read_csv("data/palangre_97_18 - palangre_97_18.csv")

longline97-2008.csv

Data Dictionary:

Variable	Description
identifier	Unique Vessel Identifier
vessel_code	Vessel Code
vessel_code2	Vessel Code (taking into account company?)
vessel_code	Name of Vessel
gross_tonnage	Gross tonnage of the vessel
vessel_length	Length of the Vessel (m)
vessel_width	Vessel Width
stanchion	Stanchions on the vessel
primary_engine_hp	Horsepower of the primary engine
second_engine_hp	Horsepower of the second engine
carrying_capacity	The carrying capacity of the vessel (units?)
year_constructed	The year the vessel was constructed
vessel_criteria	The criteria for the vessel
vessel_classification	The classification of the vessel

palangre_87_97 - palangre_87_97.csv

The data has 35030 observations and 93 variables.

Data Dictionary:

Variable	Description
CBU_ORAC	Vessel Code: 76 vessels
crucero	Trip? Range: 1-55
pesqueria	Fishery
fecha	Date
Dia	Day
Mes	Month
Year	Year
lance	Haul
latcalini	Latitude
loncalini	Longitude
aquas	Waters (management zone?)
zmin	Minimum depth (m)
zmax	Maximum depth (m)
nanzuelo	Number of hooks (anzuelos)
lonlmad	Mid longitude
Total.de.peso	Total weight (kg)
X0 - X999	Fish Species (see reference table)

palangre_97_18 - palangre_97_18.csv

The data has 62178 observations and 134 variables.

Data Dictionary:

Variable	Description
CBU_ORAC	Vessel Code: 76 vessels
crucero	Trip? Range: 1-55
pesqueria	Fishery
fecha	Date
Dia	Day
Mes	Month
Year	Year
lance	Haul
latcalini	Latitude
loncalini	Longitude
latcalini_new	Latitude (new)
loncalini_new	Longitude (new)
aquas	Waters (management zone?)
zmin	Minimum depth (m)
zmax	Maximum depth (m)
nanzuelo	Number of hooks (anzuelos)
zmin_new	Minimum depth (m) new
zmax_new	Maximum depth (m) new
lonlmad	Mid longitude
Total.de.peso	Total weight (kg)
X0 - X999	Fish Species (see reference table)

Part I: Longline Data

Exploring the vessels

How many vessels are in the data set? What is the vessel code?

Vessel Code

longline %>%
  distinct(vessel_code)

## # A tibble: 10 × 1
##    vessel_code
##          <dbl>
##  1      400062
##  2      400065
##  3      400075
##  4      400082
##  5      400088
##  6      400118
##  7      400061
##  8      400081
##  9      400133
## 10      400512

Vessel Code2

longline %>%
  distinct(vessel_code2)

## # A tibble: 10 × 1
##    vessel_code2
##           <dbl>
##  1       400062
##  2       400065
##  3       400075
##  4       400082
##  5       400088
##  6       400118
##  7       400061
##  8       400081
##  9       400133
## 10           NA

How complete is the vessel data?

longline %>%
  select(vessel_code, 
         vessel_code2, 
         vessel_code, 
         gross_tonnage, 
         vessel_length, 
         vessel_width, 
         primary_engine_hp, 
         second_engine_hp, 
         carrying_capacity) %>%
  visdat::vis_miss()

What are their dimensions (length, width, gross tonnage) and carrying capacity? What is the relationship between these variables?

longline %>%
  group_by(vessel_code) %>%
  distinct(vessel_code, vessel_length, vessel_width, gross_tonnage, carrying_capacity) %>%
  ggplot(aes(x = vessel_width, y = vessel_length, label = vessel_code)) +
  geom_point(aes(size = gross_tonnage)) +
  geom_text(check_overlap = TRUE, 
            size = 3,
            hjust = 0, 
            nudge_x = 0.1) +
  lims(x = c(8, 10.5)) +
  labs(x = "Vessel Width (m)", 
       y = "Vessel Length (m)",
       size = "Gross Tonnage (tonnes)")

longline %>%
  group_by(vessel_code) %>%
  distinct(vessel_code, vessel_length, vessel_width, gross_tonnage, carrying_capacity) %>%
  mutate(height = carrying_capacity,
         width = carrying_capacity) %>%
  ggplot(aes(x = vessel_width, y = vessel_length, label = vessel_code, size = I(carrying_capacity/6000))) +
  geom_emoji(emoji = "26f4", show.legend = TRUE) +
  geom_text(check_overlap = TRUE, 
            size = 3,
            hjust = 0, 
            nudge_x = 0.1) +
  lims(x = c(8, 10.5)) +
  labs(x = "Vessel Width (m)", 
       y = "Vessel Length (m)",
       size = "Carrying Capacity")

Observations:

One of the ships has a small carrying capacity for its size. One of the ships is much smaller than the others: 400082.
There is also one vessel for which we are missing a vessel code.
Vessel 400088 has only 2 entries.

What is their vessel classification?

longline %>%
  select(vessel_code, 
         gross_tonnage, 
         vessel_length, 
         vessel_width, 
         vessel_classification, 
         year_constructed) %>%
  visdat::vis_miss()

unique(longline$vessel_classification)

## [1] "NA"     NA       "casual"

Observations:

vessel_classification doesn’t give us any information.

longline %>%
  group_by(vessel_code) %>%
  distinct(vessel_code, vessel_length, vessel_width, gross_tonnage, carrying_capacity, year_constructed)

## # A tibble: 10 × 6
## # Groups:   vessel_code [10]
##    vessel_code gross_tonnage vessel_length vessel_width carrying_capacity
##          <dbl>         <dbl>         <dbl>        <dbl>             <dbl>
##  1      400062          505.          44.5         8.4               350 
##  2      400065          753           53.4         9.5               200 
##  3      400075          537           46.8         8.5               571 
##  4      400082          292           26.6         8                  72 
##  5      400088          653.          48.5         9.2               364.
##  6      400118          752           52.2         9.5               550 
##  7      400061          465.          46.3         8.35              350 
##  8      400081          292           26.6         8                  72 
##  9      400133           NA           NA          NA                  NA 
## 10      400512           NA           NA          NA                  NA 
## # … with 1 more variable: year_constructed <dbl>

When were the vessels constructed?

longline %>%
  group_by(vessel_code) %>%
  distinct(vessel_code, vessel_length, vessel_width, gross_tonnage, carrying_capacity, year_constructed) %>%
  ggplot(aes(x = year_constructed, y = gross_tonnage, label = vessel_code)) +
  geom_point(aes(size = carrying_capacity)) +
  geom_text(check_overlap = TRUE, 
            size = 3,
            hjust = 0, 
            nudge_x = 1) +
  labs(x = "Year Constructed", 
       y = "Gross Tonnage (tonnes)",
       size = "Carrying Capacity") +
  xlim(1965, 2000)

When were the vessels active?

longline <- longline %>%
  mutate(trip_return_date_clean = lubridate::as_datetime("1900-01-01") + days(round(trip_return_date)),
         trip_leaving_date_clean = lubridate::as_datetime("1900-01-01") + days(round(trip_leaving_date)),
         trip_duration = trip_return_date_clean - trip_leaving_date_clean,
         vessel_code = as.factor(vessel_code)) # Not sure here what to use for the origin for the date.

longline %>%
  distinct(vessel_code, trip_number, .keep_all = TRUE) %>%
  ggplot() +
  geom_point(aes(x = trip_leaving_date_clean, y = vessel_code)) +
  geom_point(aes(x = trip_return_date_clean, y = vessel_code)) +
  labs(y = "Vessel Code", x = "Date")

Observations

We have a lot more data early on in the 1997-2001 period than in the second management period.

How did the duration of trips vary by boat? by year?

longline %>%
  distinct(vessel_code, trip_number, .keep_all = TRUE) %>%
  ggplot(aes(x = trip_duration, y = as.factor(vessel_code), fill = as.factor(vessel_code))) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Trip Duration (Days)", 
       fill = "Vessel Code")

What were the trip statistics (number of trips made and the minimum, mean, and maximum trip duration)?

longline %>%
  group_by(vessel_code) %>%
  select(vessel_code, trip_number, trip_duration, haul_number) %>%
  distinct(vessel_code, trip_number, haul_number, .keep_all = TRUE) %>%
  summarise(max_number_trips = max(trip_number),
            max_trip_duration = max(trip_duration),
            min_trip_duration = min(trip_duration),
            mean_trip_duration = round(mean(trip_duration), 2),
            max_hauls = max(haul_number))

## # A tibble: 10 × 6
##    vessel_code max_number_trips max_trip_duration min_trip_duration
##    <fct>                  <dbl> <drtn>            <drtn>           
##  1 400061                    10  63 days          19 days          
##  2 400062                    11  60 days           8 days          
##  3 400065                     8  73 days          33 days          
##  4 400075                     8  88 days          19 days          
##  5 400081                    28  27 days           5 days          
##  6 400082                    29 161 days           5 days          
##  7 400088                     4  67 days          60 days          
##  8 400118                     8  91 days          24 days          
##  9 400133                    14   9 days           4 days          
## 10 400512                     8   6 days           5 days          
## # … with 2 more variables: mean_trip_duration <drtn>, max_hauls <dbl>

Doublecheck your workflow for this one to see if it makes sense.

How did the haul weight vary for each ship over time?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000) %>%
  ggplot(aes(x = total_capture_kg, y = vessel_code, fill = vessel_code)) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Total Catch (1000 kg)", 
       fill = "Vessel Code") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

Fishing effort in hauls over time

longline %>%
  group_by(Year, vessel_code, Month) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = Month, 
             y = hauls, 
             group = vessel_code, 
             color = vessel_code)) +
  geom_line() +
  scale_color_viridis_d() + 
  coord_polar() +
  facet_wrap(~Year) +
  labs(color = "Vessel Code")

The same plot above without the coord_polar

longline %>%
  group_by(Year, vessel_code, Month) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = Month, 
             y = hauls, 
             group = vessel_code, 
             color = vessel_code)) +
  geom_line() +
  scale_color_viridis_d() + 
  facet_wrap(~Year) +
  labs(color = "Vessel Code")

Create an interactive map of fishing haul locations

longline <- longline %>%
  mutate(startlondd = -startlondd,
         startlatdd = -startlatdd,
         endlondd = -endlondd, 
         endlatdd = -endlatdd,
         group = as.factor(paste(vessel_code, trip_number, haul_number)))

pal <- colorNumeric(
  palette = "Oranges",
  domain = longline$Genypterus_blacodes)


longline$labels <- sprintf("<strong>Vessel Code: %s</strong><br/>%g proportion of catch", 
                  longline$vessel_code, longline$Genypterus_blacodes) %>% lapply(htmltools::HTML)

(start_position_map <- leaflet(data = longline) %>%
  addProviderTiles(providers$Esri.OceanBasemap) %>%
  addCircleMarkers(data = longline[longline$vessel_code == 400062,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400062,]$labels,
                   radius = 2,
                   group = "Vessel 400062") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400065,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400065,]$labels,
                   radius = 2,
                   group = "Vessel 400065") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400075,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400075,]$labels,
                   radius = 2,
                   group = "Vessel 400075") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400082,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400082,]$labels,
                   radius = 2,
                   group = "Vessel 400082") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400088,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400088,]$labels,
                   radius = 2,
                   group = "Vessel 400088") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400118,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400118,]$labels,
                   radius = 2,
                   group = "Vessel 400118") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400061,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400061,]$labels,
                   radius = 2,
                   group = "Vessel 400061") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400081,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400081,]$labels,
                   radius = 2,
                   group = "Vessel 400081") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400133,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400133,]$labels,
                   radius = 2,
                   group = "Vessel 400133") %>%
    addCircleMarkers(data = longline[longline$vessel_code == 400512,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes), 
                   label = longline[longline$vessel_code == 400512,]$labels,
                   radius = 2,
                   group = "Vessel 400512") %>%
    addLegend(position = "bottomright",
              pal = pal,
              values = ~Genypterus_blacodes,
              title = "Proportion of catch that is pink cusk-eel",
              opacity = 1)) %>%
    addLayersControl(
    overlayGroups = c("Vessel 400065", "Vessel 400062", "Vessel 400075", 
                      "Vessel 400082", "Vessel 400088", "Vessel 400118",
                      "Vessel 400061", "Vessel 400081", "Vessel 400133",
                      "Vessel 400512"),
    options = layersControlOptions(collapsed = FALSE)
    )

(end_position_map <- leaflet(data = longline) %>%
  addProviderTiles(providers$Esri.OceanBasemap) %>%
  addCircleMarkers(lng = ~endlondd, 
                   lat = ~endlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(Genypterus_blacodes),
                   label = longline$labels,
                   radius = 2) %>%
    addLegend(position = "bottomright",
              pal = pal,
              values = ~Genypterus_blacodes,
              title = "Proportion of catch that is pink cusk-eel",
              opacity = 1))

Catch Composition: What are the most common species?

What are the highest proportions of species across the different hauls?

longline %>%
  select(vessel_code, trip_number, leaving_port, landing_port, haul_number, Genypterus_blacodes, Merluccius_australis, total_catch, starts_with(match = "P_")) %>%
  pivot_longer(cols = c(Merluccius_australis, starts_with(match = "P_"), Genypterus_blacodes), names_to = "prop_species", values_to = "proportion") %>%
  filter(proportion > 0) %>%
  group_by(vessel_code, trip_number) %>%
  slice_max(order_by = proportion, n = 10) %>%
  ungroup() %>%
  select(prop_species) %>%
  unique()

## # A tibble: 16 × 1
##    prop_species        
##    <chr>               
##  1 Genypterus_blacodes 
##  2 Merluccius_australis
##  3 P_trama             
##  4 P_granadero1        
##  5 P_lenguado.2        
##  6 P_reineta           
##  7 P_peje2             
##  8 P_varios            
##  9 P_brotula           
## 10 P_cojinoba.2        
## 11 P_merluza1          
## 12 P_merluza2          
## 13 P_coji              
## 14 P_granadero2        
## 15 P_bacalao.1         
## 16 P_raya56

How often do species come up in the hauls? Presence/Absence

longline %>%
  select(vessel_code, trip_number, leaving_port, landing_port, haul_number, total_catch, Merluccius_australis, Genypterus_blacodes, starts_with(match = "P_")) %>%
  pivot_longer(cols = c(Merluccius_australis, starts_with(match = "P_"), Genypterus_blacodes), names_to = "prop_species", values_to = "proportion") %>%
  filter(proportion > 0) %>%
  group_by(prop_species) %>%
  summarise(n = n()) %>%
  arrange(desc(n))

## # A tibble: 77 × 2
##    prop_species             n
##    <chr>                <int>
##  1 Genypterus_blacodes  11329
##  2 Merluccius_australis 11075
##  3 P_bacalao.1           4787
##  4 P_cojinoba.2          2634
##  5 P_varios              2150
##  6 P_reineta             1533
##  7 P_brotula             1067
##  8 P_cab2                 410
##  9 P_merluza1             359
## 10 P_coji                 265
## # … with 67 more rows

How does the weight of congrio dorado and merluza_comun/meluza_del_sur vary in hauls?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  select(vessel_code, trip_number, leaving_port, landing_port, haul_number, total_catch, merluza_comun, congrio_dorado, merluza_del_sur, bacalao_de_profundidad, Year) %>%
  pivot_longer(cols = c(merluza_comun, congrio_dorado, merluza_del_sur, bacalao_de_profundidad), names_to = "species", values_to = "weight_kg") %>%
  ggplot(aes(x = weight_kg, y = vessel_code, fill = species)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Weight (kg)", 
       fill = "Species") #+

  #scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

How complete is the fishing information?

visdat::vis_dat(longline[, c("J_time_line_set", "J_time_laying_line", "J_time_start_haul", "J_time_end_haul", "c_laying_time", "c_soak_time", "c_haul_time", "min_depth", "max_depth", "mid_depth", "max_min", "hook_type", "number_of_hooks", "bait", "hook_size")])

What is the range in the soak time?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = c_soak_time, y = vessel_code)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Soak Time (hours)")

What is the range in haul time?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = c_haul_time, y = vessel_code)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Haul Time (hours)")

What is the range in the min depth?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = min_depth, y = vessel_code)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Min Depth (m)")

What is the range in the max depth?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = max_depth, y = vessel_code)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Max Depth (m)") +
  facet_wrap(~man_zone)

What is the range in the mid depth?

longline %>%
  filter(total_capture_kg > 0 & total_capture_kg < 20000, vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = mid_depth, y = vessel_code)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Mid Depth (m)") +
  facet_wrap(~man_zone)

Looking at the hauls and filtering out only the groups that have the most:

longline %>%
  select(vessel_code, vessel_name, trip_number, leaving_port, landing_port, haul_number, total_catch, Merluccius_australis, Genypterus_blacodes, starts_with(match = "P_"), man_zone) %>%
  pivot_longer(cols = c(Merluccius_australis, starts_with(match = "P_"), Genypterus_blacodes), names_to = "prop_species", values_to = "proportion") %>%
  filter(proportion > 0, prop_species %in% c("Genypterus_blacodes", "Merluccius_australis", "P_bacalao.1", "P_cojinoba.2", "P_reineta", "P_brotula", "P_cab2", "P_merluza1", "P_coji"), vessel_name != "ISLA SOFIA") %>%
  ggplot(aes(x = proportion, y = vessel_code, fill = prop_species)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Proportion of Catch") +
  facet_wrap(~man_zone)

Looking at the haul proportions:

longline %>%
  select(vessel_code, trip_number, leaving_port, landing_port, haul_number, Genypterus_blacodes, Merluccius_australis, total_catch, starts_with(match = "P_")) %>%
  pivot_longer(cols = c(Merluccius_australis, starts_with(match = "P_"), Genypterus_blacodes), names_to = "prop_species", values_to = "proportion") %>%
  filter(proportion > 0) %>%
  group_by(vessel_code, trip_number)

## # A tibble: 37,804 × 8
## # Groups:   vessel_code, trip_number [120]
##    vessel_code trip_number leaving_port landing_port haul_number total_catch
##    <fct>             <dbl>        <dbl>        <dbl>       <dbl>       <dbl>
##  1 400062                1           57           57           1        7269
##  2 400062                1           57           57           1        7269
##  3 400062                1           57           57           1        7269
##  4 400062                1           57           57           2        6458
##  5 400062                1           57           57           2        6458
##  6 400062                1           57           57           2        6458
##  7 400062                1           57           57           3        6881
##  8 400062                1           57           57           3        6881
##  9 400062                1           57           57           4        5333
## 10 400062                1           57           57           4        5333
## # … with 37,794 more rows, and 2 more variables: prop_species <chr>,
## #   proportion <dbl>

Part II: Palangre 87-97 Data

Exploring the vessels

How many vessels are in the data set?

Vessel Code

palangre87_97 %>%
  distinct(CBU_ORAC)

## # A tibble: 76 × 1
##    CBU_ORAC
##       <dbl>
##  1   400061
##  2   400062
##  3   400063
##  4   400064
##  5   400065
##  6   400066
##  7   400067
##  8   400068
##  9   400069
## 10   400070
## # … with 66 more rows

When were the vessels active?

palangre87_97 <- palangre87_97 %>%
  mutate(fecha = lubridate::dmy(fecha),
         CBU_ORAC = as.factor(CBU_ORAC))

str(palangre87_97$fecha)

##  Date[1:35030], format: "1988-01-01" "1988-01-02" "1988-01-03" "1988-01-04" "1988-01-05" ...

ggplot(data = palangre87_97) +
  geom_density_ridges(aes(x = fecha, y = CBU_ORAC, fill = CBU_ORAC), alpha = 0.75) +
  labs(y = "Vessel Code", x = "Date")

How did the duration of trips vary by boat? by year?

palangre87_97 %>%
  distinct(CBU_ORAC, trip_number, .keep_all = TRUE) %>%
  ggplot(aes(x = trip_duration, y = as.factor(vessel_code), fill = as.factor(vessel_code))) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Trip Duration (Days)", 
       fill = "Vessel Code")

What were the trip statistics (number of trips made and the minimum, mean, and maximum trip duration)?

palangre87_97 %>%
  group_by(vessel_code) %>%
  select(vessel_code, trip_number, trip_duration, haul_number) %>%
  distinct(vessel_code, trip_number, haul_number, .keep_all = TRUE) %>%
  summarise(max_number_trips = max(trip_number),
            max_trip_duration = max(trip_duration),
            min_trip_duration = min(trip_duration),
            mean_trip_duration = round(mean(trip_duration), 2),
            max_hauls = max(haul_number))

Doublecheck your workflow for this one to see if it makes sense.

How did the haul weight vary for each ship over time?

palangre87_97 %>%
  filter(Total.de.peso >= 0, Year <= 1992) %>%
  drop_na(CBU_ORAC) %>%
  ggplot(aes(x = Total.de.peso, y = CBU_ORAC, fill = CBU_ORAC)) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Total Catch (1000 kg)", 
       fill = "Vessel Code") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

palangre87_97 %>%
  filter(Total.de.peso > 0, Year > 1992) %>%
  ggplot(aes(x = Total.de.peso, y = CBU_ORAC, fill = CBU_ORAC)) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Total Catch (1000 kg)", 
       fill = "Vessel Code") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

Fishing effort in hauls over time

palangre87_97 %>%
  group_by(Year, CBU_ORAC, Mes) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = Mes, 
             y = hauls, 
             group = CBU_ORAC, 
             color = CBU_ORAC)) +
  geom_line() +
  scale_color_viridis_d() + 
  coord_polar() +
  facet_wrap(~Year) +
  labs(color = "Vessel Code")

The same plot above without the coord_polar

palangre87_97 %>%
  group_by(Year, CBU_ORAC, Mes) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = Mes, 
             y = hauls, 
             group = CBU_ORAC, 
             color = CBU_ORAC)) +
  geom_line() +
  scale_color_viridis_d() + 
  facet_wrap(~Year) +
  labs(color = "Vessel Code") +
  scale_x_continuous(breaks = c(3, 6, 9, 12))

How much has each vessel fished?

palangre87_97 %>%
  group_by(CBU_ORAC) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = fct_reorder(CBU_ORAC, hauls),
             y = hauls)) +
  geom_col() +
  scale_color_viridis_d() +
  coord_flip() +
  labs(x = "Vessel Code")

Create an interactive map of fishing haul locations

palangre87_97 <- palangre87_97 %>%
  mutate(startlatdd = -latcalini/10000,
         startlondd = -loncalini/10000,
         group = as.factor(paste(CBU_ORAC, crucero, lance)),
         P_X6 = X6/Total.de.peso)

pal <- colorNumeric(
  palette = "Oranges",
  domain = palangre87_97$P_X6)


palangre87_97$labels <- sprintf("<strong>Vessel Code: %s</strong><br/>%g proportion of catch", 
                  palangre87_97$CBU_ORAC, palangre87_97$P_X6) %>% lapply(htmltools::HTML)

(start_position_map <- leaflet(data = palangre87_97) %>%
  addProviderTiles(providers$Esri.OceanBasemap) %>%
  addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400062,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(P_X6), 
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400062,]$labels,
                   radius = 2,
                   group = "Vessel 400062") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400065,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(P_X6), 
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400065,]$labels,
                   radius = 2,
                   group = "Vessel 400065") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400075,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400075,]$labels,
                   radius = 2,
                   group = "Vessel 400075") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400082,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400082,]$labels,
                   radius = 2,
                   group = "Vessel 400082") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400088,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400088,]$labels,
                   radius = 2,
                   group = "Vessel 400088") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400118,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400118,]$labels,
                   radius = 2,
                   group = "Vessel 400118") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400061,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400061,]$labels,
                   radius = 2,
                   group = "Vessel 400061") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400081,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400081,]$labels,
                   radius = 2,
                   group = "Vessel 400081") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400133,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400133,]$labels,
                   radius = 2,
                   group = "Vessel 400133") %>%
    addCircleMarkers(data = palangre87_97[palangre87_97$CBU_ORAC == 400512,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre87_97[palangre87_97$CBU_ORAC == 400512,]$labels,
                   radius = 2,
                   group = "Vessel 400512") %>%
    addLegend(position = "bottomright",
              pal = pal,
              values = ~P_X6,
              title = "Proportion of catch that is pink cusk-eel",
              opacity = 1)) %>%
    addLayersControl(
    overlayGroups = c("Vessel 400065", "Vessel 400062", "Vessel 400075",
                      "Vessel 400082", "Vessel 400088", "Vessel 400118",
                      "Vessel 400061", "Vessel 400081", "Vessel 400133",
                      "Vessel 400512"),
    options = layersControlOptions(collapsed = FALSE)
    )

Catch Composition: What are the most common species?

What are the highest proportions of species across the different hauls?

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(CBU_ORAC, Year) %>%
  arrange(desc(peso)) %>%
  distinct(species_code, .keep_all = TRUE) %>%
  slice_max(order_by = peso, n = 5) %>%
  ungroup() %>%
  select(species_code) %>%
  unique()

## # A tibble: 25 × 1
##    species_code
##    <chr>       
##  1 X6          
##  2 X2          
##  3 X200        
##  4 X99         
##  5 X25         
##  6 X5          
##  7 X8          
##  8 X4          
##  9 X37         
## 10 X27         
## # … with 15 more rows

Organized by proportion

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(CBU_ORAC, Year) %>%
  arrange(desc(prop)) %>%
  distinct(species_code, .keep_all = TRUE) %>%
  slice_max(order_by = prop, n = 5) %>%
  ungroup() %>%
  select(species_code) %>%
  unique()

## # A tibble: 25 × 1
##    species_code
##    <chr>       
##  1 X2          
##  2 X6          
##  3 X99         
##  4 X25         
##  5 X200        
##  6 X5          
##  7 X999        
##  8 X8          
##  9 X4          
## 10 X37         
## # … with 15 more rows

How often do species come up in the hauls? Presence/Absence

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  arrange(desc(n))

## # A tibble: 29 × 2
##    species_code     n
##    <chr>        <int>
##  1 X2           25302
##  2 X6           24569
##  3 X37          14679
##  4 X99          10957
##  5 X5            9350
##  6 X4            7583
##  7 X8            3726
##  8 X56           2023
##  9 X81           1427
## 10 X96           1332
## # … with 19 more rows

species_list <- palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  arrange(desc(n)) %>%
  select(species_code)


species_list_100 <- palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 100) %>%
  arrange(desc(n)) %>%
  select(species_code)

species_list_1000 <- palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 1000) %>%
  arrange(desc(n)) %>%
  select(species_code)


species_list_10000 <- palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 10000) %>%
  arrange(desc(n)) %>%
  select(species_code)

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0, species_code %in% c(species_list_10000$species_code)) %>%
  group_by(Year, species_code) %>%
  summarise(species_weight = sum(peso)) %>%
  ggplot() +
  geom_area(aes(x = Year, y = species_weight, fill = species_code, group = species_code), alpha = 0.7) +
  scale_color_viridis_d() +
  labs(fill = "Species Code",
       y = "Species Weight (kg)")

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(species_code %in% species_list_1000$species_code) %>%
  filter(peso > 0, !(species_code %in% c(species_list_10000$species_code))) %>%
  group_by(Year, species_code) %>%
  summarise(species_weight = sum(peso)) %>%
  ggplot() +
  geom_area(aes(x = Year, y = species_weight, fill = species_code, group = species_code), alpha = 0.7) +
  scale_color_viridis_d() +
  labs(fill = "Species Code",
       y = "Species Weight (kg)")

How does the weight of congrio dorado and merluza_comun/meluza_del_sur vary in hauls?

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(Total.de.peso > 0, Total.de.peso < 20000, species_code %in% c("X2", "X6")) %>%
  ggplot(aes(x = peso, y = as.factor(species_code), fill = as.factor(species_code))) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Weight (1000 kgs)", 
       fill = "Species") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

What is the range in the min depth?

palangre87_97 %>%
  filter(Total.de.peso > 0 & Total.de.peso < 20000,
         zmin < 2500) %>%
  ggplot(aes(x = zmin, y = CBU_ORAC)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Min Depth (m)")

What is the range in the max depth?

palangre87_97 %>%
  filter(Total.de.peso > 0 & Total.de.peso < 20000,
         zmax < 2500) %>%
  ggplot(aes(x = zmax, y = CBU_ORAC)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Min Depth (m)")

Looking at the hauls and filtering out only the groups that have the most:

palangre87_97 %>%
  select(CBU_ORAC, crucero, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0, species_code %in% c(species_list_10000$species_code), CBU_ORAC %in% c(unique(longline$vessel_code))) %>%
  ggplot(aes(x = prop, y = CBU_ORAC, fill = species_code)) +
  ggridges::geom_density_ridges(alpha = 0.5) +
  scale_color_viridis_d() +
  labs(y = "Vessel Code",
       x = "Proportion of Catch",
       fill = "Species Code")

Part III: palangre 1997-2018

Exploring the vessels

How many vessels are in the data set?

Vessel Code

palangre97_18 %>%
  distinct(CBU_ORAC)

## # A tibble: 60 × 1
##    CBU_ORAC
##       <dbl>
##  1   941225
##  2   400075
##  3   942434
##  4   940391
##  5   940239
##  6   941612
##  7   400065
##  8   400061
##  9   941096
## 10   400099
## # … with 50 more rows

When were the vessels active?

palangre97_18 <- palangre97_18 %>%
  mutate(fecha = lubridate::dmy(fecha),
         CBU_ORAC = as.factor(CBU_ORAC)) %>%
  filter(Year < 2030, Year > 1991)

str(palangre97_18$fecha)

##  Date[1:62176], format: "2018-07-13" "2018-07-13" "2018-07-13" "2018-07-12" "2018-07-11" ...

ggplot(data = palangre97_18) +
  geom_density_ridges(aes(x = fecha, y = CBU_ORAC, fill = CBU_ORAC), alpha = 0.75) +
  labs(y = "Vessel Code", x = "Date")

How did the duration of trips vary by boat? by year?

palangre97_18 %>%
  distinct(CBU_ORAC, trip_number, .keep_all = TRUE) %>%
  ggplot(aes(x = trip_duration, y = as.factor(vessel_code), fill = as.factor(vessel_code))) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Trip Duration (Days)", 
       fill = "Vessel Code")

What were the trip statistics (number of trips made and the minimum, mean, and maximum trip duration)?

palangre97_18 %>%
  group_by(vessel_code) %>%
  select(vessel_code, trip_number, trip_duration, haul_number) %>%
  distinct(vessel_code, trip_number, haul_number, .keep_all = TRUE) %>%
  summarise(max_number_trips = max(trip_number),
            max_trip_duration = max(trip_duration),
            min_trip_duration = min(trip_duration),
            mean_trip_duration = round(mean(trip_duration), 2),
            max_hauls = max(haul_number))

Doublecheck your workflow for this one to see if it makes sense.

How did the haul weight vary for each ship over time?

palangre97_18 %>%
  filter(Total.de.peso >= 0, Year <= 2002) %>%
  drop_na(CBU_ORAC) %>%
  ggplot(aes(x = Total.de.peso, y = CBU_ORAC, fill = CBU_ORAC)) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Total Catch (1000 kg)", 
       fill = "Vessel Code") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

palangre97_18 %>%
  filter(Total.de.peso > 0, Year > 2002) %>%
  ggplot(aes(x = Total.de.peso, y = CBU_ORAC, fill = CBU_ORAC)) + 
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Total Catch (1000 kg)", 
       fill = "Vessel Code") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

Fishing effort in hauls over time

palangre97_18 %>%
  group_by(Year, CBU_ORAC, Mes) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = Mes, 
             y = hauls, 
             group = CBU_ORAC, 
             color = CBU_ORAC)) +
  geom_line() +
  scale_color_viridis_d() + 
  facet_wrap(~Year) +
  labs(color = "Vessel Code") +
  scale_x_continuous(breaks = c(3, 6, 9, 12))

How much has each vessel fished?

palangre97_18 %>%
  group_by(CBU_ORAC) %>%
  summarise(hauls = n()) %>%
  ggplot(aes(x = fct_reorder(CBU_ORAC, hauls),
             y = hauls)) +
  geom_col() +
  scale_color_viridis_d() +
  coord_flip() +
  labs(x = "Vessel Code")

Create an interactive map of fishing haul locations

palangre97_18 <- palangre97_18 %>%
  mutate(startlatdd = -latcalini/10000,
         startlondd = -loncalini/10000,
         group = as.factor(paste(CBU_ORAC, pesqueria, lance)),
         P_X6 = X6/Total.de.peso)

pal <- colorNumeric(
  palette = "Oranges",
  domain = palangre97_18$P_X6)


palangre97_18$labels <- sprintf("<strong>Vessel Code: %s</strong><br/>%g proportion of catch", 
                  palangre97_18$CBU_ORAC, palangre97_18$P_X6) %>% lapply(htmltools::HTML)

(start_position_map <- leaflet(data = palangre97_18) %>%
  addProviderTiles(providers$Esri.OceanBasemap) %>%
  addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400062,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(P_X6), 
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400062,]$labels,
                   radius = 2,
                   group = "Vessel 400062") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400065,], 
                   lng = ~startlondd, 
                   lat = ~startlatdd, 
                   clusterOptions = NULL, 
                   color = ~pal(P_X6), 
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400065,]$labels,
                   radius = 2,
                   group = "Vessel 400065") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400075,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400075,]$labels,
                   radius = 2,
                   group = "Vessel 400075") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400082,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400082,]$labels,
                   radius = 2,
                   group = "Vessel 400082") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400088,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400088,]$labels,
                   radius = 2,
                   group = "Vessel 400088") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400118,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400118,]$labels,
                   radius = 2,
                   group = "Vessel 400118") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400061,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400061,]$labels,
                   radius = 2,
                   group = "Vessel 400061") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400081,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400081,]$labels,
                   radius = 2,
                   group = "Vessel 400081") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400133,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400133,]$labels,
                   radius = 2,
                   group = "Vessel 400133") %>%
    addCircleMarkers(data = palangre97_18[palangre97_18$CBU_ORAC == 400512,],
                   lng = ~startlondd,
                   lat = ~startlatdd,
                   clusterOptions = NULL,
                   color = ~pal(P_X6),
                   label = palangre97_18[palangre97_18$CBU_ORAC == 400512,]$labels,
                   radius = 2,
                   group = "Vessel 400512") %>%
    addLegend(position = "bottomright",
              pal = pal,
              values = ~P_X6,
              title = "Proportion of catch that is pink cusk-eel",
              opacity = 1)) %>%
    addLayersControl(
    overlayGroups = c("Vessel 400065", "Vessel 400062", "Vessel 400075",
                      "Vessel 400082", "Vessel 400088", "Vessel 400118",
                      "Vessel 400061", "Vessel 400081", "Vessel 400133",
                      "Vessel 400512"),
    options = layersControlOptions(collapsed = FALSE)
    )

Catch Composition: What are the most common species?

What are the highest proportions of species across the different hauls?

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(CBU_ORAC, Year) %>%
  arrange(desc(peso)) %>%
  distinct(species_code, .keep_all = TRUE) %>%
  slice_max(order_by = peso, n = 5) %>%
  ungroup() %>%
  select(species_code) %>%
  unique()

## # A tibble: 35 × 1
##    species_code
##    <chr>       
##  1 X6          
##  2 X2          
##  3 X37         
##  4 X5          
##  5 X4          
##  6 X27         
##  7 X96         
##  8 X1          
##  9 X99         
## 10 X120        
## # … with 25 more rows

Organized by proportion

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(CBU_ORAC, Year) %>%
  arrange(desc(prop)) %>%
  distinct(species_code, .keep_all = TRUE) %>%
  slice_max(order_by = prop, n = 5) %>%
  ungroup() %>%
  select(species_code) %>%
  unique()

## # A tibble: 36 × 1
##    species_code
##    <chr>       
##  1 X37         
##  2 X6          
##  3 X2          
##  4 X5          
##  5 X4          
##  6 X27         
##  7 X96         
##  8 X1          
##  9 X99         
## 10 X24         
## # … with 26 more rows

How often do species come up in the hauls? Presence/Absence

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  arrange(desc(n))

## # A tibble: 54 × 2
##    species_code     n
##    <chr>        <int>
##  1 X37          47558
##  2 X6           18157
##  3 X2           18056
##  4 X99           3411
##  5 X5            3391
##  6 X96           3167
##  7 X27           2981
##  8 X17           2789
##  9 X4            1896
## 10 X16           1861
## # … with 44 more rows

species_list <- palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  arrange(desc(n)) %>%
  select(species_code)


species_list_100 <- palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 100) %>%
  arrange(desc(n)) %>%
  select(species_code)

species_list_1000 <- palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 1000) %>%
  arrange(desc(n)) %>%
  select(species_code)


species_list_10000 <- palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0) %>%
  group_by(species_code) %>%
  summarise(n = n()) %>%
  filter(n >= 10000) %>%
  arrange(desc(n)) %>%
  select(species_code)

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0, species_code %in% c(species_list_10000$species_code)) %>%
  group_by(Year, species_code) %>%
  summarise(species_weight = sum(peso)) %>%
  ggplot() +
  geom_area(aes(x = Year, y = species_weight, fill = species_code, group = species_code), alpha = 0.7) +
  scale_color_viridis_d() +
  labs(fill = "Species Code",
       y = "Species Weight (kg)")

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(species_code %in% species_list_1000$species_code) %>%
  filter(peso > 0, !(species_code %in% c(species_list_10000$species_code))) %>%
  group_by(Year, species_code) %>%
  summarise(species_weight = sum(peso)) %>%
  ggplot() +
  geom_area(aes(x = Year, y = species_weight, fill = species_code, group = species_code), alpha = 0.7) +
  scale_color_viridis_d() +
  labs(fill = "Species Code",
       y = "Species Weight (kg)")

How does the weight of congrio dorado and merluza_comun/meluza_del_sur vary in hauls?

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(Total.de.peso > 0, Total.de.peso < 20000, species_code %in% c("X2", "X6")) %>%
  ggplot(aes(x = peso, y = as.factor(species_code), fill = as.factor(species_code))) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Weight (1000 kgs)", 
       fill = "Species") +
  facet_wrap(~Year) +
  scale_x_continuous(breaks = c(0, 5000, 10000, 15000, 20000), labels = c("0", "5", "10", "15", "20"))

What is the range in the min depth?

palangre97_18 %>%
  filter(Total.de.peso > 0 & Total.de.peso < 20000,
         zmin < 2500) %>%
  ggplot(aes(x = zmin, y = CBU_ORAC)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Min Depth (m)")

What is the range in the max depth?

palangre97_18 %>%
  filter(Total.de.peso > 0 & Total.de.peso < 20000,
         zmax < 2500) %>%
  ggplot(aes(x = zmax, y = CBU_ORAC)) +
  geom_density_ridges(alpha = 0.75) +
  scale_fill_viridis_d() +
  labs(y = "Vessel Code",
       x = "Min Depth (m)")

Looking at the hauls and filtering out only the groups that have the most:

palangre97_18 %>%
  select(CBU_ORAC, lance, Year, fecha, Total.de.peso, starts_with(match = "X")) %>%
  pivot_longer(cols = starts_with(match = "X"), names_to = "species_code", values_to = "peso") %>%
  mutate(prop = peso/Total.de.peso) %>%
  filter(peso > 0, species_code %in% c(species_list_10000$species_code), CBU_ORAC %in% c(unique(longline$vessel_code))) %>%
  ggplot(aes(x = prop, y = CBU_ORAC, fill = species_code)) +
  ggridges::geom_density_ridges(alpha = 0.5) +
  scale_color_viridis_d() +
  labs(y = "Vessel Code",
       x = "Proportion of Catch",
       fill = "Species Code")

Chile - Catch Intention Analysis

Laurie Baker

6/14/2022

Libraries

Data

longline97-2008.csv

palangre_87_97 - palangre_87_97.csv

palangre_97_18 - palangre_97_18.csv

Part I: Longline Data

Exploring the vessels

Fishing effort in hauls over time

Create an interactive map of fishing haul locations

Catch Composition: What are the most common species?

Part II: Palangre 87-97 Data

Exploring the vessels

Fishing effort in hauls over time

How much has each vessel fished?

Create an interactive map of fishing haul locations

Catch Composition: What are the most common species?

Part III: palangre 1997-2018

Exploring the vessels

Fishing effort in hauls over time

How much has each vessel fished?

Create an interactive map of fishing haul locations

Catch Composition: What are the most common species?